youtube-audience-research

youtube-audience-research is a skill for Claude Code, Codex from tubealfred/mcp. It costs 45 tokens per session (507 once invoked), scanned A, original, MIT.

A research workflow for studying public YouTube audience activity, including comments, replies, channel videos, and transcripts. YouTube is a video-sharing platform, and this workflow uses public data rather than private creator analytics.

In plain words
What is it for?
Finding recurring questions, objections, sentiment patterns, customer wording, and content opportunities from public comments and videos.
Why use it?
It helps reveal what viewers repeatedly ask about, object to, or care about without identifying anonymous viewers or accessing private channel data.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit Finding recurring questions, objections, sentiment patterns, customer wording, and content opportunities from public comments and videos.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/tubealfred/mcp/youtube-audience-research
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add tubealfred/mcp --skill youtube-audience-research
Clone the repo
git clone --depth 1 https://github.com/tubealfred/mcp

Made for: Claude Code, Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for youtube-audience-research

README.md
[![agentmods](https://agentmods.dev/badge/skills/tubealfred/mcp/youtube-audience-research/github.svg)](https://agentmods.dev/skills/tubealfred/mcp/youtube-audience-research)
Your own site
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Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for youtube-audience-research

Your own site · 80×15
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Per session 45 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 507 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00045 $0.00507
Opus 5.5 $0.00018 $0.00203
Sonnet 5.5 $0.00009 $0.00101
Haiku 4.5 $0.00005 $0.00051

Measured 2mo ago against content hash 6d42bb914ac9, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

youtube-audience-research scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2mo ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

skills/youtube-audience-research/SKILL.md · 43 lines

How it starts

The opening of the file, as written. The whole thing — 43 lines — stays where its author put it; the contents beside it link to each section on GitHub.

YouTube audience research with TubeAlfred

Connect to the read-only TubeAlfred MCP server at https://mcp.tubealfred.com/. The workflow reads public YouTube data; it cannot identify anonymous viewers, access private Studio analytics, or take actions on a channel.

Start from the user's scope

  • For one video, resolve the URL when needed, then use youtube_comments_list.
  • For a specific comment thread, use youtube_comment_replies only when replies matter to the question.
  • For a channel-wide study, use youtube_channel_get and youtube_channel_videos to establish the video set, then sample comments only from the videos needed.
  • Add youtube_video_transcript when comparing audience language with what the creator actually said.

Pagination rules

Initial comment and reply tools return continuation tokens when more public results exist. Use youtube_comments_page or youtube_comment_replies_page with the matching token. Stop when one of these conditions is met:

  1. the requested comment count is reached;
  2. no continuation token remains;
  3. the evidence is saturated and additional pages no longer change the themes;
  4. the available credit budget would be exceeded.

Comment results are credit-metered when non-empty. Fetching every page by default is wasteful. Explain sampling limits instead of silently implying complete coverage.

Analysis method

  1. Preserve direct comment text separately from classifications.
  2. Group repeated questions, desired outcomes, objections, praise, confusion, and terminology.
  3. Report counts from the fetched sample, not from the entire audience unless complete coverage was explicitly obtained.
  4. Include representative quotations only when useful, and avoid exposing unnecessary personal identifiers.
  5. Compare comments with transcript moments when the user asks what triggered a reaction.

Boundaries

Do not claim sentiment represents all viewers. Do not infer demographics, purchase history, identity, or intent beyond the public text. Authentication, permissions, unavailable data, rate limits, and upstream failures are operational outcomes; buying credits does not resolve them.

Read the full file on GitHub · 43 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 2mo ago First seen · 43 lines · 45 tokens per session scan A 6d42bb914ac9

Subscribe to this mod's changes

youtube-audience-research is a skill published in the GitHub repository tubealfred/mcp (2 stars, last pushed 1mo ago), licensed MIT. It adds 45 tokens to every session and 507 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.

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